Lint fixes

web/apps/photos/src/services/embeddingService.ts

@@ -13,11 +13,14 @@ import type {
     PutEmbeddingRequest,
 } from "types/embedding";
 import { EnteFile } from "types/file";
-import { getLatestVersionEmbeddings,getLatestVersionFileEmbeddings } from "utils/embedding";
+import {
+    getLatestVersionEmbeddings,
+    getLatestVersionFileEmbeddings,
+} from "utils/embedding";
+import { FileML } from "utils/machineLearning/mldataMappers";
 import { getLocalCollections } from "./collectionService";
 import { getAllLocalFiles } from "./fileService";
 import { getLocalTrashedFiles } from "./trashService";
-import { FileML } from "utils/machineLearning/mldataMappers";
 
 const ENDPOINT = getEndpoint();
 
@@ -154,7 +157,7 @@ export const syncEmbeddings = async () => {
 export const syncFileEmbeddings = async () => {
     const models: EmbeddingModel[] = ["file-ml-clip-face"];
     try {
-        let allEmbeddings :FileML[] = await getFileMLEmbeddings();
+        let allEmbeddings: FileML[] = await getFileMLEmbeddings();
         const localFiles = await getAllLocalFiles();
         const hiddenAlbums = await getLocalCollections("hidden");
         const localTrashFiles = await getLocalTrashedFiles();
@@ -179,7 +182,6 @@ export const syncFileEmbeddings = async () => {
                 const newEmbeddings = await Promise.all(
                     response.diff.map(async (embedding) => {
                         try {
-                          
                             const worker =
                                 await ComlinkCryptoWorker.getInstance();
                             const fileKey = fileIdToKeyMap.get(
@@ -196,7 +198,7 @@ export const syncFileEmbeddings = async () => {
 
                             return {
                                 ...decryptedData,
-                                updatedAt: embedding.updatedAt
+                                updatedAt: embedding.updatedAt,
                             } as unknown as FileML;
                         } catch (e) {
                             let hasHiddenAlbums = false;
@@ -229,7 +231,6 @@ export const syncFileEmbeddings = async () => {
     }
 };
 
-
 export const getEmbeddingsDiff = async (
     sinceTime: number,
     model: EmbeddingModel,
@@ -263,7 +264,7 @@ export const putEmbedding = async (
     try {
         const token = getToken();
         if (!token) {
-            log.info('putEmbedding failed: token not found');
+            log.info("putEmbedding failed: token not found");
             throw Error(CustomError.TOKEN_MISSING);
         }
         const resp = await HTTPService.put(

web/apps/photos/src/services/machineLearning/machineLearningService.ts

@@ -1,12 +1,13 @@
 import log from "@/next/log";
 import { APPS } from "@ente/shared/apps/constants";
-import { CustomError, parseUploadErrorCodes } from "@ente/shared/error";
 import ComlinkCryptoWorker from "@ente/shared/crypto";
+import { CustomError, parseUploadErrorCodes } from "@ente/shared/error";
 import "@tensorflow/tfjs-backend-cpu";
 import "@tensorflow/tfjs-backend-webgl";
 import * as tf from "@tensorflow/tfjs-core";
 import { MAX_ML_SYNC_ERROR_COUNT } from "constants/mlConfig";
 import downloadManager from "services/download";
+import { putEmbedding } from "services/embeddingService";
 import { getLocalFiles } from "services/fileService";
 import { EnteFile } from "types/file";
 import {
@@ -16,14 +17,13 @@ import {
     MlFileData,
 } from "types/machineLearning";
 import { getMLSyncConfig } from "utils/machineLearning/config";
+import { LocalFileMlDataToServerFileMl } from "utils/machineLearning/mldataMappers";
 import mlIDbStorage from "utils/storage/mlIDbStorage";
 import FaceService from "./faceService";
 import { MLFactory } from "./machineLearningFactory";
 import ObjectService from "./objectService";
 import PeopleService from "./peopleService";
 import ReaderService from "./readerService";
-import { LocalFileMlDataToServerFileMl } from "utils/machineLearning/mldataMappers";
-import { putEmbedding } from "services/embeddingService";
 
 class MachineLearningService {
     private initialized = false;
@@ -213,19 +213,18 @@ class MachineLearningService {
         // existingFiles.sort(
         //     (a, b) => b.metadata.creationTime - a.metadata.creationTime
         // );
-        console.time('getUniqueOutOfSyncFiles');
+        console.time("getUniqueOutOfSyncFiles");
         syncContext.outOfSyncFiles = await this.getUniqueOutOfSyncFilesNoIdx(
             syncContext,
-            [...existingFilesMap.values()]
+            [...existingFilesMap.values()],
         );
-        log.info('getUniqueOutOfSyncFiles');
+        log.info("getUniqueOutOfSyncFiles");
         log.info(
-            'Got unique outOfSyncFiles: ',
+            "Got unique outOfSyncFiles: ",
             syncContext.outOfSyncFiles.length,
-            'for batchSize: ',
-            syncContext.config.batchSize
+            "for batchSize: ",
+            syncContext.config.batchSize,
         );
-
     }
 
     private async syncFiles(syncContext: MLSyncContext) {
@@ -441,8 +440,8 @@ class MachineLearningService {
     }
 
     private async persistOnServer(mlFileData: MlFileData, enteFile: EnteFile) {
-        const serverMl = LocalFileMlDataToServerFileMl(mlFileData)
-            log.info(mlFileData);
+        const serverMl = LocalFileMlDataToServerFileMl(mlFileData);
+        log.info(mlFileData);
 
         const comlinkCryptoWorker = await ComlinkCryptoWorker.getInstance();
         const { file: encryptedEmbeddingData } =

web/apps/photos/src/types/embedding.tsx

@@ -5,7 +5,7 @@
  * embeddings on the server. However, we should be prepared to receive an
  * {@link EncryptedEmbedding} with a model value distinct from one of these.
  */
-export type EmbeddingModel = "onnx-clip" |"file-ml-clip-face";
+export type EmbeddingModel = "onnx-clip" | "file-ml-clip-face";
 
 export interface EncryptedEmbedding {
     fileID: number;
@@ -24,7 +24,6 @@ export interface Embedding
     embedding?: Float32Array;
 }
 
-
 export interface GetEmbeddingDiffResponse {
     diff: EncryptedEmbedding[];
 }

web/apps/photos/src/utils/machineLearning/mldataMappers.ts

@@ -1,4 +1,9 @@
-import { Face, FaceDetection, Landmark, MlFileData } from "types/machineLearning";
+import {
+    Face,
+    FaceDetection,
+    Landmark,
+    MlFileData,
+} from "types/machineLearning";
 import { ClipEmbedding } from "types/machineLearning/data/clip";
 
 export interface FileML extends ServerFileMl {
@@ -12,7 +17,6 @@ class ServerFileMl {
     public faceEmbedding: ServerFaceEmbeddings;
     public clipEmbedding?: ClipEmbedding;
 
-
     public constructor(
         fileID: number,
         faceEmbedding: ServerFaceEmbeddings,
@@ -154,12 +158,14 @@ class ServerFaceBox {
     }
 }
 
-
 export function LocalFileMlDataToServerFileMl(
     localFileMlData: MlFileData,
 ): ServerFileMl {
-    if(localFileMlData.errorCount > 0 && localFileMlData.lastErrorMessage !== undefined) {
-        return null
+    if (
+        localFileMlData.errorCount > 0 &&
+        localFileMlData.lastErrorMessage !== undefined
+    ) {
+        return null;
     }
     const imageDimensions = localFileMlData.imageDimensions;
     const fileInfo = new ServerFileInfo(
@@ -176,20 +182,13 @@ export function LocalFileMlDataToServerFileMl(
         const detection: FaceDetection = face.detection;
         const box = detection.box;
         const landmarks = detection.landmarks;
-        const newBox = new ServerFaceBox(
-            box.x,
-            box.y,
-            box.width,
-            box.height,
-        );
+        const newBox = new ServerFaceBox(box.x, box.y, box.width, box.height);
         const newLandmarks: Landmark[] = [];
         for (let j = 0; j < landmarks.length; j++) {
-            newLandmarks.push(
-                 {
-                    x: landmarks[j].x,
-                    y: landmarks[j].y,
-                 } as Landmark,
-                );
+            newLandmarks.push({
+                x: landmarks[j].x,
+                y: landmarks[j].y,
+            } as Landmark);
         }
 
         const newFaceObject = new ServerFace(
@@ -203,7 +202,11 @@ export function LocalFileMlDataToServerFileMl(
         );
         faces.push(newFaceObject);
     }
-    const faceEmbeddings = new ServerFaceEmbeddings(faces, 1,localFileMlData.lastErrorMessage);
+    const faceEmbeddings = new ServerFaceEmbeddings(
+        faces,
+        1,
+        localFileMlData.lastErrorMessage,
+    );
     return new ServerFileMl(
         localFileMlData.fileId,
         faceEmbeddings,
@@ -213,7 +216,6 @@ export function LocalFileMlDataToServerFileMl(
     );
 }
 
-
 // // Not sure if this actually works
 // export function ServerFileMlToLocalFileMlData(
 //     serverFileMl: ServerFileMl,